chapter v methodology - shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/12677/12/12_chapter...

Status of Wetlands in Kollam District 185

CHAPTER V

METHODOLOGY

5.1 The Need for Methodology

As mention in the first chapter the aim of the research is to identify the driving forces that

lead to detrimental changes in the wetlands and its immediate vicinity using satellite images

and Geographic Information Systems. The research is related with the status of wetlands in

Kollam district, the research strategy was devised in line with the research questions,

objectives, and research problems. The research methodologies adopted for this study are

contemporary in nature. The change in wetland is reflective in general land use change so the

study should be start from analyzing the change in land use of the area.

5.2 Primary Data and Sources

For the purpose of the research the major primary source are the top sheets of 1:50000 scale

prepared during 1972-74 periods with numbers 58C/8, 58C/12, 58C/16, 58D/9, 58D/13,

58G/4, and 58H/1. The geologic map of the study area was prepared on the basis of geologic

map of Kollam district (Scale 1:250,000,000) which was obtained from Kerala State Land

Use Board (KSLUB). The soil amp was obtained from Kerala state land use board, which

was prepared by soil survey organization Thiruvananthapuram. The land use change analysis

during 1974 and 2010 were prepared with the help of Land Sat image (MSS) taken on 9th

February 1973, Land Sat Images (TM) Taken on 25th February 1990, ASTER Image taken on

8th February 2004, GEO eye Taken on 21st January 2011. The census details and monthly

total rainfall from 1975 to 2010 obtained from IMD Thiruvananthapuram were also used.


Table 5-1: Maps Used

Maps Properties Scale

Topographical maps

58C/8, 58C/12, 58C/16,

58D/9, 58D/13, 58G/4, and

58H/1

1:50000

Geologic map Prepared by Land use Board 1:250,000,000

Political map of Kollam land Survey department Government of Kerala

1:250,000,000

Soil map Soil map prepared by soil survey organization

1:250,000,000

Land Sat Image (MSS) Taken on 9th February 1973 68 m X 82 m

Land Sat Image (TM) Taken on 25th February1990 30 m X 30 m

ASTER Image Taken on 8th February 2004 15 m X 15 m

GEOEYE 2010 Taken on 21st January 2010

For the study the topographical, satellite images, soil, political, geologic maps were used (see

table 5-1).

5.3 GPS Survey

Hand held GPS Magellan(model) was used for GPS survey around 100 ground control points

were located using GPS. These ground control points were used for the software training for

supervised classification of ASTER images. For the preparation of maps and geospatial

analysis ArcGIS 9.2 and ILWIS 3.5 software were used. Analysis of socio economic data was

done using SPSS software.

5.3.1 ArcGIS 9.2

ArcGIS 9.2 is used for spatial organizing and special analyzing of the geo coded data.

ArcGIS is a suite consisting of a group of geographic information system (GIS) software

products produced by ESRI. ArcGIS is a system for working with maps and geographic

information. ArcGIS is built around the geo-database, which uses an object-relational

database approach for storing spatial data. A geo-database is a "container" for holding


datasets, tying together the spatial features with attributes. The geo-database can also contain

topology information, and can model behavior of features, such as road intersections, with

rules on how features relate to one another. When working with geo-database, it is important

to understand about feature classes which are a set of features, represented with points, lines,

or polygons. With shape files, each file can only handle one type of feature. A geo-database

can store multiple feature classes or type of features within one file. (ESRI, 2010)

5.3.2.ILWIS 3.5

ILWIS (Integrated Land and Water Information System) is a GIS / Remote sensing software

for both vector and raster processing. ILWIS features include digitizing, editing, analysis and

display of data as well as production of quality maps. ILWIS was initially developed and

distributed by ITC Enschede (International Institute for Geo-Information Science and Earth

Observation) in the Netherlands for use by its researchers and students, but since 1 July 2007

it has been distributed under the terms of the GNU General Public License and is thus free

software. Having been used by many students, teachers and researchers for more than two

decades, ILWIS is one of the most user-friendly integrated vector and raster software

programs currently available. ILWIS has some very powerful raster analysis modules, a high-

precision and flexible vector and point digitizing module, a variety of very practical tools, as

well as a great variety of user guides and training modules all available for downloading. The

current version is ILWIS 3.5 is Open source. (ILWIS, 2010)

5.4. Preparation of Maps Using ArcGIS

The base map was prepared from the Survey of India topographic sheets bearing numbers

58C/8, 58C/12, 58C/16, 58D/9, 58D/13, 58G/4, and 58H/1. different layers of information

such as administrative boundaries, contours, drainage, geology, soil, transport and

communication networks, and land use were traced separately. These traced sheets were

converted to raster format by scanning them. These raster images were then imported in


ArcGIS software and were properly geo referenced using the geo referencing tool. The geo-

referenced map sheets were assigned WGS84 coordinate system.

Using Arc catalogue module separate .shp files were created for administrative boundaries,

contours, drainage, transport and communication networks, and land use. These .shp files

were opened in Arcmap module and raster - vector conversion was done using the editor tool

5.5. Land Use Maps From Satellite Image

Land use map of the district for the year 2004 was prepared with the ASTER image using

ILWIS software. Since the ASTER image is already geo-coded the geo-referenced vector

data of administrative boundaries of Kollam district could be incorporated along with the

satellite image. The ground control points taken with GPS were imported and training sites

were identified accordingly. This helped in the supervised classification of the image. The

shape file generated after the supervised classification was exported to ArcGIS software for

land use change detection and analysis.

5.6 Standardized Precipitation Index (SPI)

Standardized Precipitation Index (SPI), a tool derived by Tom McKee (1993) et al., a

measure of rainfall conditions has been calculated from the available rainfall data of Kollam

district. For the actual SPI calculation one should need the continuous rainfall data of at least

30 years. The main purpose of using this method in this research is only to analyze that there

was no extreme drought in the months for which satellite data was acquired. The SPI value is

given in (Table 6-1) and Rainfall data in (Appendix 3)

SPI is calculated based on the equation

Where, Xi is monthly rainfall record of the station; Xm is rainfall mean; and σ is the standard deviation.


Monthly rainfall data of 35 years of Kollam available from IMD was used as an input to

calculate SPI. The classification of meteorological conditions using SPI values are presented

in the Table 5.2

Table 5-2: Classification of Meteorological Conditions Using SPI

SPI Value Class

2.0 and more Extremely wet

1.5 to 1.99 Very wet

1.0 to 1.49 Moderately wet

-.99 to .99 Near normal

-1.0 to -1.49 Moderately dry

-1.5 to -1.99 Severely dry

-2 and less Extremely dry

Source (McKee et al., 1993)

5.7 Markov Analysis

The Russian mathematician Andrei Andreyevich Markov (1856–1922) developed the theory

of Markov chains in his paper ‘Extension of the Limit Theorems of Probability Theory to a

Sum of Variables Connected in a Chain’ (Markov, 1907). A Markov chain is defined as a

stochastic process fulfilling the Markov property with a discrete state space and a discrete or

continuous parameter space. Markov chain represents a system of elements making

transitions from one state to another over time. It is a random process characterized as

memory less: the next state depends only on the current state and not on the entire past.

Markov chains have many applications as statistical models of real-world processes. (Brown

et al., 2000)


5.7.1 Perpetration of Map for Markov Analysis

For the Markov analysis one has to prepare at least two maps of area with different time

scale. For the better understanding the researcher first draw the Sasthamkotta Lake from the

latest image after drawing it then created a 500 meter buffer. Then drawn a square box

touching all the extreme edges of the buffer. After creating this started making the polygons

for the land use categories. For the analysis of the data the land use of the selected are is

divided in to nine groups. For the accurate analysis the data of 1974, 1990, 2004 and 2010

were selected.

5.7.2 Analyzing Land Use Change Using Markov

The Markov module analyzes a pair of land cover images and outputs a transition probability

matrix, a transition probability matrix, a transition area matrix, and a set of conditional

probability images. The transition probability matrix is a text file that records the probability

that each land cover category will change to every other category. The transition area matrix

is a text file that records the probability that each land cover category will change to every

other category. The area matrix is a text file that records the number of pixels that area

expected to change from each land cover type over the specified number of time units. In

both of these files, the rows represent the older land cover categories and the column

represents the newer categories.

The conditional probability images report the probability that each land cover type would be

found at each pixel after the specified number of time units. These images are calculated as

projection from the later of the two input land cover images. The output conditional

probability images can be used as direct input for specification of the prior probabilities in

Maximum Likelihood Classification of remotely sensed imagery. A raster group file is also

created listing all the conditional probability images.


5.7.3 Markov Operation

Enter the name of three land cover images to be compared. All the land cover categories must

be numbered from one (1) with no intermediate gaps; In addition, the class values in both

images must match exactly.

Next enter a prefix for the output conditional probability images. The name of each

conditional probability image will begin with this prefix. If legend captions exist in the later

cover image, the caption will follow the prefix. If no legend captions exist in the later image,

the filenames will consist of the prefix followed by “class_# where # is the class value. In

addition, a raster group file (.rgf) of the probability images will be created with this prefix as

its name.

Enter the number of time periods between the first and second input land cover images and

the number of tiles periods to project into future for output images. The specific unit of time

used (years, decades, etc) is not important, but it must be same in both cases. Change is

distributed among multiple time periods by simple division, i.e. assuming a constant rate of

transition.

Next choose one of three options for how background areas should be treated. (The module

assumes that the value 0 in a land cover image indicates background).Assign 0.0 to the

background areas in the output probability images to keep those areas as background. Assign

equal probabilities to give the background areas the probability 1/ [number of classes] in each

of the output probability images. Assign relative frequencies to give higher output

probabilities to land cover that occupy more area in the second input image. For use with

maximum likelihood classification, it is normal to assign relative frequencies to background

pixel. Finally, enter the proportional error associated with the input maps. Again for use with

Maximum Likelihood classification, it would be normal to assign a proportional error of

around 0.15 (15%).


5.7.4 Markov Note

1 the output conditional probability images are named with the given prefix plus_class_#

where# is the class value in the output landcover image. In the present research the researcher

uses 8 land use categories 1 to 8 and the specified output prefix NEXT the output probability

files will be NEXTclass_1 ….. etc to NEXTclass_8.

The transition probability matrix file is stored with a name derived from a combination of the

prefix and the phrase transition_probability.txt. The transition area file is stored with a name

derived from a combination of the prefix and the phrase transition_area.txt.

Proportional error express the probability that the landcover classes in the input maps are

incorrect (i.e.,0.0 would indicate a perfectly accurate map). The output conditional

probability are multiplied by (1-proportional error) to produce the final output conditional

probability values.

The transition probability matrix is the result of cross-tabulation of the two image adjusted

by the proportional error. The transition area matrix is the result of cross-tabulation of each

column in the transition probability matrix by the number of cells of the corresponding land

use in the later image.

5.7.5 Markov Macro Command

Running this module in macro mode requires 8 parameters.

X (to indicate that command line mode is being used)

Input first land cover image (the earlier of the two images to compare)

Input second land cover image (later of the two images to compare)

Input third land cover image (to compare over all accuracy of the result)

Background option (1-asign0.0, 2- equal probabilities, 3- relative frequency)

Proportional errors of the input land cover images)


Number of time periods between first and second land cover image and output images.

(Clark Labs, 2010)

5.8 SPSS 9

SPSS 9 (originally, Statistical Package for the Social Sciences) SPSS used for tabulating,

organizing, and analyzing the socio economic data which was collected. SPSS is a computer

program used for survey authoring and deployment (IBM SPSS Data Collection), data

mining (IBM SPSS Modeler), text analytics, statistical analysis, and collaboration &

deployment (batch & automated scoring services) (IBM SPSS, 2009).

5.9 Preparation of Questionnaire and Socio Economic Survey

The preparation of questionnaire was used on the basis of research questions and problem

statement. The questionnaire was prepared on three stages. First procedure was to find out the

similar questionnaires and what type of socio economic data was needed for the successful

completion of the thesis. Then take a pilot survey in the ample study area for greater accuracy

of the questionnaire. In this regard the researcher first prepared a sample questionnaire

containing 45 questions after the pilot survey the number of questions were refined to 33. The

family details along with the ownership, type, cultivation, paddy cultivation, land conversion,

the use of Kayal, etc are the major questions that included in the questionnaire (appendix 1)

5.9.1 Field Visit

The geographical study will never be completed without field visits. The field visits in the

study area were conducted in four phases.

5.9.2 First Phase

The first phase of field visit conducted during 2004 January 5 to 12 in Kollam district and

prepared route map using the GPS coordinates. The first field visit was undertaken after the

preparation of base map (map no) the first visit was mainly confined to the areas surrounding

Sasthamkotta Lake and Ashtamudi Estuary. During the field visit the researcher was able to


get the details regarding 8 villages in Kollam district namely Sasthamkotta, Kunnathoor,

West Kallada, East Kallada, Sooranad South, Sooranad north, Valakom, and Kottukkal

Villages..

5.9.3 Second Phase

The second phase of the field visit commenced in the month of October 2005. This field visit

was mainly for the preparation of land use map using satellite images using supervised

sampling technique. During this visit about 100 ground control points were taken using GPS

and prepared a data base of each marked points.

5.9.4 Third Phase

The third phase of the field visit was conducted after preparing Draft questionnaire. The main

purpose of the visit was to conduct the pilot survey to check the validity and enquire the

possibility of modifying the questionnaire for the better result regarding the present condition

of wetland in Kollam district. The study conducted in the Kunnathur Taluk of Kollam district.

With sample study the researcher was able to refine the prepared questionnaire.

5.9.5 Fourth Phase

The fourth phase was conducted during September 2006. The fourth phase of research was

conducted for detailed data collection using prepared questionnaire. The data was collected

using random sampling techniques. Three villages, Kunnathur, Sasthamkotta, and West

Kallada, surrounding the Sasthamkotta Lake were selected for the final survey. A total of

3880 households were surveyed in the fourth stage between January and March 2007. Of this

1760 households are from Sasthamkotta village (table 6.7). This comprises of 23.84 % of the

total households of this village. 1312 households were surveyed from Kunnathur that is

21.86% of total households of Kunnathur. 808 Households were surveyed from West Kallda

that is 19.12% of the total households of the village.


CHAPTER VI

STATUS OF WETLANDS OF KOLLAM DISTRICT

This chapter analyses the various aspects, both physical and human, which have direct or

indirect effect on the status of the Kollam district. This includes an analysis of rainfall, land

use changes, demographic changes, and socio-economic conditions of the population who are

directly depended on the wetland.

6.1 Rainfall Analysis

The rainfall analysis shows that Kollam district receives an average annual rainfall of 2491

cm. (Appendix 1 and 2) January is the least rainfall month of Kollam with an average of

9.67 cm of rainfall and June is the most rainfall month which receives maximum rainfall with

an average of 409 cm. Kollam district receives an average of 421.92 cm of rainfall during

pre-monsoon season, 1194.41 cm during the monsoon season and 583.78 cm during the post

monsoon season.

6.1.1 Mean Monthly Rainfall

Of the 35 years selected for the study only 12 years recorded more than the average rain fall.

Of these 35 years the year 1999 recorded the maximum rainfall with 2919 mm. 1977, 1981,

1991, 1992, 1997, 1998, 1999, 2001, 2005, 2006, 2007, 2008 were the years which recorded

more than the average rainfall. The least rainfall is recorded in 1996 with 1526.5 mm (Figure

6-1), (Table 6-1), (Appendix 5). The trend line of the last 35 years of rainfall shows a very

slight increase over the time. This increase is insignificant (cf. regression equation, figure

This is

Status of Wetlands in Kollam District

19

6

F

igur

e 6-

1: M

ean

Ann

ual R

ainf

all o

f Kol

lam

(19

75-2

010)

(IM

D, 1

975

-201

0)

y =

17

9.1

e0

.00

1x

R²

= 0

.00

61

00

0

20

00

30

00 1

97

51

98

01

98

51

99

01

99

52

00

02

00

52

01

02

01

5

R a i n f a l l i n m m

Ye

ars

Me

an

An

nu

al

Ra

infa

ll

Ra

infa

ll

Exp

on

. (R

ain

fall)


The general rainfall trend of the district reveals that in both pre-monsoon as well as post-

monsoon rainfall shows a very slight decline over the years while the monsoon rain fall

shows an increasing trend. The annual total rain fall from 1975 to 2010 shows that there is a

very slight increase. The trend line shows a considerable increase over the years. Although

there seems to be an upward trend this is statistically insignificant (cf. the regression

equation). This means the chance of the reduction of wetted areas due to lack of sufficient

rainfall is very meager. Hence it can be assumed that the changes that have happened to the

wetlands of Kollam may be due to the other anthropogenic factors.

6.1.2 Pre-Monsoon Rainfall

In the 34 instances of pre monsoon rainfall, the year 2004 has recorded maximum rainfall of

855 mm. 16 instances recorded above average pre monsoon rainfall. The least recorded

rainfall is 167 mm in 1979 (Figure 6-2). The trend line shows a considerable increase over

the years. Although there seems to be an upward trend this is statistically insignificant (cf. the

regression equation)

6.1.3 Post-Monsoon Rainfall

In the 35years of post monsoon rainfall the average is 594.63mm; there was more than

average rainfall recorded in 18 instances. The least rainfall is recorded in 1988 with 246.8

mm rainfall. The highest post monsoon rainfall was recorded in 1993 with the rainfall of

1063 mm (Figure 6-3). The post monsoon rainfall shows a decreasing trend over the years.

The trend line shows a considerable increase over the years. Although there seems to be a

slight downward trend this is statistically insignificant (cf. the regression equation)


19

8

Fig

ure

6-2:

Pre

-mon

soon

rai

nfal

l (19

75-2

010)

y =

7E

-08

e0

.01

1x

R²

= 0

.07

2

10

0

20

0

30

0

40

0

50

0

60

0

70

0

80

0 19

75

19

80

19

85

19

90

19

95

20

00

20

05

20

10

Pre

-mo

nso

on

Pre

-mo

nso

on

Exp

on

. (P

re-m

on

soo

n)


19

9

Fig

ure

6-3:

Pos

t-M

onso

on R

ainf

all (

1975

-201

0)

y =

25

25

.e-8

E-0

x

R²

= 0

.00

0

20

0

30

0

40

0

50

0

60

0

70

0

80

0

90

0

10

00

11

00 1

97

51

98

01

98

51

99

01

99

52

00

02

00

52

01

0

Po

st -

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nso

on

Po

st m

on

soo

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xpo

n. (

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st m

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20

0

Fig

ure

6-4:

Mon

soon

Rai

nfal

l (19

75-2

010)

y =

27

36

1e

-0.0

0x

R²

= 0

.00

4

60

0

80

0

10

00

12

00

14

00

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Exp

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. (M

on

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n)


6.1.4 Monsoon Rainfall

The monsoons recorded an average rainfall of 1213.9 mm. Of the 35 instances of rainfall 15

instances recorded more than average rainfall. The year 1991 recorded maximum monsoon

rainfall of 1958.4 mm, while the least monsoon rainfall was recorded in the year 2002 with

661.2 mm (Figure 6-4). The trend line shows a very slight decrease over the years.

6.1.5 Standardized Precipitation Index (SPI) Analysis (1975-2010)

The SPI analysis of 1975 to 2010 reveals that the Kollam district has a near normal rainfall

throughout the period. Of the total 420 months through out the period, extreme wet months

occur 14 times but there is only one occurrence of extreme dry spell that is in the month of

April 1975. 19 instances have the occurrence of very wet conditions; of this 11 instances are

during monsoon season. While severe dry conditions occur in 17 instances, of this only five

instances happened in monsoon season. The occurrence of moderate wet conditions stretched

over 17 years of this 7 years are recorded in monsoon, while moderate dry conditions prevails

for 40 instances, of this only 12 instances are recorded during the monsoon season. The

remaining 312 i.e. 74.28% instances are related with near normal SPI conditions. The years

1989, 1997, 2001, and 2010 were recorded the normal SPI throughout the year. The years

1976, 2003 and 2008 have recorded near normal SPI for 11 months. This analysis reveals that

the overall rain fall condition of Kollam district is near normal from 1975 to 2010. (Table 6-

1, Appendix 1, Appendix 2)


20

2

Tab

le 6

-1: S

PI V

alue

s of

Kol

lam

197

5-20

10

Yea

r S

PI J

an

SP

I Feb

S

PI M

arch

S

PI A

pr

SP

I may

S

PI Ju

n S

PI J

UL

SP

I Aug

S

PI S

ept

SP

I Oct

S

PI N

ov

SP

I Dec

1975

-0

.530

602

-0.8

2049

-1

.008

01

-2.0

094

-1.6

1522

1

.859

82

0.51

1629

1.53

9069

0.

9255

23

0.28

9379

2.49

6721

-0

.047

27

1976

0.

5415

746

-0.8

2049

0.

0043

08

0.61

8493

-0.5

2843

-1

.279

73

0.28

1227

0.51

7213

-1

.173

3 -1

.205

41

1.02

8863 0.

8052

64

1977

-0

.530

602

-0.5

2964

-0

.426

47

-1.5

688

3.09

4639

0

.391

633

0.19

059

-0.8

5559

-0

.702

17

1.03

8357

0.

2288

81

0.09

4822

1978

-0

.530

602

-0.8

2049

0.

3704

66

-1.3

4582

0.

6019

37

-0.1

7298

0.

2990

15 1.

0571

37

-1.1

3597

-0

.977

49

4.06

7328 -0

.554

72

1979

-0

.530

602

-0.0

8415

-1

.008

01

-1.1

6001

-1

.083

36

0.55

0882

-0

.615

82

-0.6

8062

-0

.702

17

-1.7

5559

1.

5866

49

0.62

2579

1980

-0

.530

602

-0.8

2049

-1

.008

01

-1.0

8037

-0

.737

33

0.59

5811

1.

4484

86 0.

8681

64

-1.3

9058

-0

.642

57

0.49

3829 1.

2843

05

1981

-0

.530

602

-0.8

2049

-0

.361

85

0.19

1122

-0

.652

74

1.65

9136

-0

.343

06

-0.3

3367

1.

4489

24

0.60

5305

0.

3301

63

-0.5

2022

1982

-0

.530

602

-0.8

2049

0.

3575

43

-1.0

5117

-0

.397

07

0.44

6546

-0

.611

58

0.84

4167

-1

.526

47

-1.5

5616

-0

.030

93

-0.2

8476

1983

-0

.530

602

0.58

9599

-0.8

3355

-1

.643

12

-0.7

4694

-1

.360

11

-0.2

9478

1.

1031

3 1.

5161

22

-1.9

9681

0.

2736

5 0.

8113

54

1984

0.

1634

913

3.63

4367

2.

9895

73

0.64

5038

-1.2

7752

0.

3172

5 -0

.715

77

-1.2

7253

-0

.537

16

-1.3

282

3 -0

.849

99

-0.5

5472

1985

0.

3948

557

-0.5

6277

-0

.814

16

-1.1

7859

0.

3744

54

1.26

975

-0.3

371

3 -1

.214

54

-1.1

733

-1.8

6892

0.

9437

27 -0

.047

27

1986

-0

.530

602

-0.4

7441

-0

.977

86

0.16

9886

-1

.043

63

-0.6

3625

-0

.835

21

1.17

512

-0.6

3721

-1

.481

45

0.67

511

-0.7

4147

1987

-0

.530

602

-0.7

4686

-1

.008

01

-0.8

9987

-1

.101

3 0

.506

951

-1.8

4491

1.

6910

48

-0.4

834

0.92

6928

-0

.262

85

4.01

6461

1988

-0

.530

602

1.69

7791

0.83

1394

-0

.469

84

-0.4

5666

0.

4700

1 0.

2032

96

-0.2

5168

1.

5318

02

-1.9

3349

-0

.301

02

0.05

4226

1989

-0

.338

739

-0.8

2049

-0

.779

7 0.

4419

7 0.

5122

26

0.38

0151

-0

.211

77

0.07

3276

0.

5775

85

0.02

2204

-0

.744

31

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7801

1990

4.

0854

007

-0.8

2049

-0

.185

23

-1.0

2197

1.

1152

16

-0.6

3325

0.

8504

56

-0.8

5059

-1

.410

74

-0.7

8122

0.

6237

35 -0

.721

17

1991

-0

.271

022

-0.7

2477

-0

.224

-0

.366

32

-0.7

0785

3.

6485

02

1.84

4914

0.71

6185

-1

.849

77

0.00

7642

0.21

4202

-0

.700

87

1992

-0

.395

169

-0.5

7014

-1

.008

01

-1.3

644

1.01

2048

1

.098

02

1.57

8934

0.16

1263

0.

2632

46

-0.5

8369

1.

2299

6 -0

.615

62

1993

-0

.530

602

0.09

9932

-0.6

5047

-0

.913

14

0.15

4661

-1

.599

23

2.14

647

-0.6

226

3 -0

.405

75

1.44

1019

1.64

9767

0.

4074

17


20

3

Yea

r S

PI J

an

SP

I Feb

S

PI M

arch

S

PI A

pr

SP

I may

S

PI Ju

n S

PI J

UL

SP

I Aug

S

PI S

ept

SP

I Oct

S

PI N

ov

SP

I Dec

1994

2.

4827

787

-0.5

9959

-0

.848

63

-0.9

9808

0.

2738

49

0.20

7423

0.

9233

04

0.93

8154

-0

.350

5 0.

1557

92 -1

.246

32

-0.4

6135

1995

-0

.530

602

0.12

9386

1.64

9865

0.

0743

25 0.

2853

84

-0.4

096

0.24

0567

-0.3

4267

-1

.128

51

-0.7

0778

1.

0266

61 -0

.778

01

1996

1.

7266

12

0.63

0097

-1.0

0801

-0

.931

72

-1.3

2238

-0

.830

94

0.21

5155

-0

.879

59

-0.6

7081

-0

.807

18

-0.3

2524

0.

6997

13

1997

-0

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45

-0.8

2049

0.

1335

4 -0

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75

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2067

0.

3586

85

0.87

4174

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7969

0.

0818

11

0.19

3146

0.14

0809

0.

3789

99

1998

-0

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602

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2049

-0

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16

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3429

-0

.122

16

0.04

418

-1.2

3757

-0

.167

69

0.98

8242

0.

4729

83

0.03

8059

0.

0846

73

1999

-0

.248

45

0.94

6724

-0.8

5724

1.

6670

11 0.

8928

59

0.19

1948

-0

.311

72

-0.8

7459

-1

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45

1.08

0142

-0

.644

49

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7801

2000

-0

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602

1.75

6699

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2708

-1

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02

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4167

0.

1485

16

-1.6

0519

3.

2178

32

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3412

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29

0.14

0809

1.90

7464

2001

0.

2594

229

0.02

6298

-0

.900

32

0.28

6683

0.05

726

0.02

9703

0.

6454

66 0.

9371

54

0.84

2645

-0

.414

65

0.27

0715

-0

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01

2002

-0

.361

311

-0.6

3641

-0

.719

39

0.61

8493

0.

0508

52

-0.6

5222

-1

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58

0.00

0286

-1

.397

3 0.

6065

71

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7386

-0

.778

01

2003

-0

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602

0.45

3376

0.75

8163

0.

2627

93 -0

.927

0.

1010

91

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1007

-0

.180

69

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6743

0.

5622

53

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3128

0.

9270

54

2004

-0

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59

1.49

8979

0.09

6924

-0

.509

66

2.40

7063

0.

1614

96

0.10

9272

-0

.887

59

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0142

-1

.173

75

-0.5

8872

-0

.696

81

2005

1.

3880

299

0.37

606

-0.9

2186

2.

9544

32 -0

.549

58

-0.3

0777

-0

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38

-1.7

0247

-0

.253

43

-1.1

1424

0.

3279

61 1.

2010

82

2006

-0

.135

59

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2049

0.

8012

4 -0

.894

56

0.73

3301

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23

-0.3

7525

0.

4672

2 1.

3802

32

1.54

2318

0.

7499

7 -0

.778

01

2007

-0

.530

602

-0.8

2049

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16

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3094

-0

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3 1

.619

199

1.80

4254

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677

0.88

3711

-0

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11

-0.3

7294

-0

.757

71

2008

-0

.530

602

0.16

9884

2.69

6646

-0

.921

1 0.

1533

79

-0.7

7203

1.

5179

46

0.28

7245

0.

1534

89

0.19

3779

-0.1

968

-0.1

2846

2009

-0

.304

881

-0.4

8914

0.

4781

59

-0.3

3181

-0

.506

64

-0.4

6751

0.

5726

18 -0

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6 0.

4051

1 -1

.194

01

0.65

3826 -0

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65

2010

0.

9478

731

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2049

-0

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47

0.20

5722

0.06

4308

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06

0.53

7041

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3371

0.

7829

14

0.46

2853

0.38

5942

0.

8133

83


6.1.5.1 The Selection and Identification of the Use of Imagery in the Light of

Standardized Precipitation Index (SPI).

Table 6-2: Satellite Images Selected for the Present Study

LANDSAT image (MSS) image taken on 9th February 1973

LANDSAT image TM taken on 25th February 1990 February

ASTER image taken on 8th February 2003

GEO EYE 2010 image taken on January 2010

These dates were selected in such a way that it coincides with the years having a normal SPI

value. The analysis of the Standardized Precipitation Index (Table 6-1) reveals that there was

no occurrence of drought in the region. Hence the wetted area of the wetlands that were

estimated through the land use change analysis may be representing realistic conditions.

6.2. Population Dynamics

The population growth of Kollam saw a considerable decrease over the last hundred years.

Until 1961 the decadal population growth rate was steadily increasing. After that the

demography started a declining trend, now it has a decadal growth rate of 9.8 % (table 6-3)

and (figure 6-5). The population density increased more than six fold over the last century

(1901- 2001) from 163 persons per sq. km to 1069 persons per sq. km.


Table 6-3: Change in Population of Kollam from 1901 to 2001

Population Growth Rate , Sex Ratio and Density

Census

year

Population per sq.km

Persons Males Females Percentage Sex Ratio Density

1901 406013 204371 201642 987 163

1911 465684 234261 231423 14.7 988 187

1921 552333 277631 274702 18.61 989 222

1931 698041 347911 350130 26.38 1006 280

1941 856585 425539 431046 22.71 1013 344

1951 1110362 556067 554295 29.63 997 446

1961 1461103 732042 729061 31.59 996 587

1971 1839265 919567 919698 25.88 1000 738

1981 2175339 1076052 1099287 18.72 1022 873

1991 2407566 1182810 1224756 10.68 1035 967

2001 2585208 1249621 1335587 9.79 1069 1038

(Census, 1981; Census of India, 1901-2001, 1971, 1991, 2001a, b; Censusof India, 1981)

The decadal growth rate of the population of Kollam shows a declining trend. The density of

population increased more than 6 fold during the last 100 years (table 6-3). This 6 fold

increase exerts tremendous pressure on the available land resources of the district. The

increase in population along with the large scale increase in the number of households and

their aspirations take the toll on the environment especially wetlands.

The decadal growth rate shows a declining trend (Table 6-3, Figure 6-5). The district

achieved single digit growth in the last decade. These demographic changes may be mainly

due to the declining birth rate over the years and also attributed to the change from

agriculture dominated societies to service dominated one.


Figure 6-5: Decadal Population Growth of Kollam (Census of India, 1901-2001)

6.2.1. Labour Status of Kollam

Labour status of Kollam shows a considerable decrease over last two decades the number of

main workers increased considerably while that increase is not visible in the case of

cultivators and agriculture labours concerned (table 6-4).

Table 6-4: Change in Labour Status of Kollam from 1981-1991

Year Main

workers Cultivators

Agriculture

Labours

Percentage of agriculture

labours and Cultivators

to main workers

1981 693341 138891 175655 45.366

1991 672712 108331 154361 39.09

2001 828566 42104 73082 21.5268

(Census of India, 1991, 2001b; Census of India, 1981)

The number of main workers shows an increase of 4.2% but this increase is not reflected in

the change of cultivators and agriculture labours (Table 6-4). In 1981 the cultivators and

agriculture labours are 45 % of the total labour force while it declined to the level of 21 % I n

0

5

10

15

20

25

30

35

1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001

Decadel Population Growth of Kollam

Decadel Growth


2001. The large scale decline is the result not only the result of increase of service and

industrial sectors but also on the large scale migration of semiskilled labours to the gulf

countries. Similar observations about this have studied by Zachariah et al. in (2003). The

boom in the number of cashew factories also resulted in the decline of labour force especially

women agricultural labours. The socio economic survey conducted in the area also reveals

the same factor. The main reason behind the decline in the agriculture labour is the result of

the cashew factories. And the remaining labours move to the bricklins which was started

functioning in the abandoned paddy fields. And also a good number of people started

exploring the possibility of sand mining from the paddy fields. They are shifted themselves

to the agriculture labours. Not only this labour activism in the form of trade unions provide

new status to the in the society and more or less continuous throughout the nineties. Similar

study conducted by Raj and Azeez (2009 ) reveals same situation.

6.2.2. Change in No. of Households

The last three decades population change is reflective in the case of number of occupied

residents. The annual increases of households are at par with the population change (table 6-

5). The obvious change in the number of occupied residents shows a considerable increase

over the last few decades. The number of occupied houses along with wells and tube wells

increase the demand on ground water and the need for more wetland to balance the water

table is of absolute necessity. The continuous increases in future will also exert pressure on

the limited resources of the district and it is high time to enquire the alternatives.


Table 6-5: Change in No. of Households (1981-2001)

Year Number of households in Kollam

Kerala % of Kerala Decadal Change

1981(excluding Pathanamthitta Taluk)

403208 4423277 9.11 %

1991 485190 5513200 8.80% 8.30

%

2001 593314 6726356 8.2 % 8.2%

(Census of India, 1991, 2001b)

6.3 Land Use Dynamics

6.3.1. Agriculture Land Use

The district saw a considerable change in the agricultural land use through the passage of

time. Table 6.6 gives an idea about the change in land utilization pattern in selected crops.

From the chart it is evident that paddy, coconut, sugarcane, and cashew have lost

considerable area. In the case of paddy there is a decrease of over -83% and more than 50%

reduction each in total food crops and area under tea. The reductions of non food growing

areas are more than 16% while there was an overall reduction of -58% in the case of total

cultivable area (table 6-6).


20

9

Tab

le 6

-6: C

hang

e in

Lan

d us

e of

Sel

ecte

d cr

ops

from 1

975-

76 to

200

5-06

Yea

r P

addy

C

ocon

ut

Rub

ber

Sug

ar

cane

T

apio

ca

Pep

per

Ban

ana

Oth

er

Pla

ntai

n

Tot

al

Foo

d cr

ops

Tea

C

offe

e T

otal

N

on-f

ood

crop

s

Tot

al

crop

ped

area

19

75-7

6 36

901

7807

3 33

995

425

4553

1 87

50

1428

43

23

2049

06

2690

42

8 14

0443

34

5349

2004

-05

8949

6615

3 36

805

21

817

1356

5 17

13

4236

86

852

1258

1159

94

2028

46

Cha

nge

from

197

5-

76 to

200

4-05

(in %

) -7

6 %

-1

8 %

9

%

-100

%

-47

%

36 %

17

%

-2 %

-5

8 %

-5

3 %

-1

00 %

-1

6 %

-5

8 %

(Eco

nom

ics

and

Sta

tistic

s, 2

006)


The area under each crop clearly shows the trend prevailing in the district over the three

decades and these changes did not happen overnight. The main decrease happened over the

area dedicated to paddy fields (table 6-5) and at the same time the area under tapioca and

plantains shows an increase up to the late nineties. There increase in area was attributed to the

decrease in the paddy fields as most of the abandoned paddy fields were converted for the

cultivation of tapioca and plantains. The changed cultivation resulted in the shortage of

ground water as the paddy fields were filled and drained for the cultivation of such crops. The

area in the tapioca cultivation and more revenue generating rubber changed the economy of

the district drastically. That resulted in the overall increase in the area dedicated to rubber.

However, there is an overall decrease of 32% in the case of net cropped area. This shows the

gradual change of districts economy from agriculture oriented one to that oriented towards

the service sector. The change in the perception of people on agriculture resulted in the

overall reduction of paddy fields in the area. As paddy is one of the most difficult crops to be

grown and the cultivator should look after the crop from the sowing stage to reaping stage

while all other crops are very easy to maintain. The ease in agriculture along with changed

labour status, and economy resulted in the reduction of wet paddy cultivation. The increase in

population resulted in the overall reduction of the net cropped area. Not only did this change

of society from joint family system to nuclear family resulted in the fragmentation of land

holdings, this fragmentation lead to the reduction of revenue generated from the holdings.

Hence, people turned to more revenue generating real estate business. The recent boom in

real estate may partly be attributed to this fragmentation. The analysis of 1991 and 2001

population and the land use dynamics reveals that the per capita availability of land holding

for each house hold in 1991 was 1 household in 1.16 cent of agriculture area. And it

increased to more than 100 families depended on 2.5 cent of agriculture field in 2001. These


changes also affect the overall quality of the environment of Kollam especially the wet

environment (Muralikrishna et al., 2001).

6.3.2. Per Capita Availability of Wetlands

The per-capita availability of paddy field in 1975-76 was about 0.6975 cent per head while in

2001 it is only 0.085 cent per persons. The per-capita availability of overall wetlands in 1970

was 7.4 cents which declined to 4.4 cents. This large scale reduction of wetland area may be

owing to the increase in population and the consequent conversion of wetlands to other land

use types. The change in lifestyle may be the main reason behind this reduction. The overall

loss of wetland at present is 1.76 ha per 100 people. The reduction in the per-capita

availability of wetland will reduce the overall quality of wetland functions. The hydrologic

functions will be seriously affected an example of which may be the frequent lack of water

availability in open wells which were traditionally perennial. Another major developmental

activity which may have significantly affected the wetlands of Kollam was the construction

of Kallada reservoir which reduced the water discharge into the wetlands, consequent to

which the brackishness of the estuary has increased (Muralikrishna et al., 2001). The people

in the area mentioned that earlier the open wells surrounding the wetland belt had fresh water

which has become salty, off late.

6.3.3. Rice Production and Productivity

The rice production in 1995-96 was 45893 tons while it was 20646 tones, which shows an

overall reduction of 55 % (table 6-5). The overall decrease of paddy production in Kollam is

greater than that of the state. The mean yield of paddy in last 10 year saw an increase of 27%,

(table 6-6) which despite the reduction in the cultivated land area, may be attributed to the

use of better high yielding variety of seedlings. This increase in the yield of paddy production

did not stop the cultivators from abandoning their paddy fields which implies that there were

other motivating driving forces for the cultivators to convert paddy fields to other land use.


Access to higher education facilities in the neighboring Kollam town, job availability in the

secondary and tertiary sectors, early 80’s gulf boom and the recent real estate boom may be

some of these driving forces.

Table 6-7: Rice Production (in Tonnes) for the Years 1995-96 and 2004-05

District Autumn Winter Summer

1995-96 2004-

2005

Variation

in% 1995-96

2004-

2005

Variation

in%

1995-

96

2004-

2005

Variation

in%

Kollam 20023 8695 -57 25836 11951 -54 34 0 -100

% of

state 5.82 3.6 5.64 3.56 0.023 0

State 344238 241824 -30 458058 335529 -27 150730 89752 -40

(Agriculture Department, 2009; Panchayath Level Statistics, 2006)

Table 6-8: Mean Yield of Paddy (Kg. /Ha) for the Years 1995-96 & 2004-05

Autumn Winter Summer

1995-

96

2004-

2005

Variation

in%

1995-

96

2004-

2005

Variation

in%

1995-

96

2004-

2005

Variation

in%

Kollam 2898 3686 27.2 3012 3394 12.7 2070 0 -100

State 2807 3494 24.5 3104 3508 10.5 3835 3823 -0.3

(Agriculture Department, 2009; Panchayath Level Statistics, 2006)

6.4. Land use changes 1973- 2004

The 1974 and 2004 data comparison shows the area under built up category had an increase

of over 155.61% while the area under mixed crops, forests, and fallow land area decreased.

The comparison of land use map prepared from Survey of India topographical sheets and

ASTER image (2004) shows remarkable variation in the total land use of the area (table 6-8),

Figure . Area under built up land use increased by 155 % from 1974 to 2004. This change

may be attributed to the increase in the number of households over the years (there was an

increase of 47% in the case of households; see table 6-3). Land fragmentation as a result of


increase in the number of nuclear families and the change from agriculture economy to

service sector based economy also contributed to the increase in built up area.

In the case of mixed crops there was a decrease of about 8% over the years. The decrease

may be due to the increase in area under rubber and built up land. The overall change in area

under rubber was more than 79.74 %. Even rocky out crops with thin soil cover was changed

to rubber plantation (Map 12, 13 and 14). A steady income from rubber plantation and its

easy maintenance force people to the change over to the cultivation of rubber from other

crops.

2111 ha decline in area under forest may be attributed to the inundation of forest area

consequent on the construction of Kallada dam where the reservoir occupies an area of 2500

ha area. The forest boundaries are clearly demarcated and the strict enforcement of forest

laws helped the preservation of forest area. The area under fallow shows a decline of 42%,

which may be attributed to the infrastructure development and conversion to rubber

plantations.

Table 6-9: Comparison of 1973 and 2004 Land Use (Area in ha)

Type of Land use 1974 2004 Change Change in %

Built up area 3533 9015 5482 155.16%

Mixed Crops 85631 79433 -6198 -7.70 %

Forest 80438 78327 -2111 -2.64 %

Rubber 30914 55556 24642 79.74 %

Fallow land 2194 1251 -943 -42 %

Total wetlands 53460 26578 -26882 -50.28 %


21

4


21

5


21

6


6.4.1 Wetland Change 1974-2004

The change of wetland was very high (table 6-8, Figure 6-4). There was an overall decrease

of over 50% in the area of wetlands. In the case of coastal wetlands the decrease was -

12.64%. The losses of paddy fields were very high with more than 55% decline in area. In

1974 wetlands were the third largest land use category in Kollam district, while it slipped to

the fourth place in 2004. (Map 15 and 16)

Table 6-10: Comparison of Wetland Loss 1974 - 2004

Type of wetland Wetland

1974

Wetland

2004

Change

Inland Wetland 46960 20901 55. 49 %

Inland wetland other than paddy 19678 12548 36%

Coastal wetlands 6500 5677 12.61%

Total wetlands 53460 26578 50.28%

6.4.2. Conversion of Paddy in Kollam between 1974 - 2004

The paddy fields saw a decrease of over 18000 ha (table6-9, figure 6-5). Of this over 13000

ha were converted to mixed vegetation, which is more than 72% of the total change in paddy.

4.8% of area was converted for planting rubber and for infrastructure development. The

changes in area under paddy stand out among the change in all other land uses.

Comparatively less price of area under wetland forced large scale conversion of paddy fields

to other land use classes. More than 2080 ha of paddy were kept fallow. Eventually these

fallow paddy fields were used for clay mining or sand mining and some fallow area was

converted to settlement. The number of families depended on agriculture is decreasing over

the period of time and these declines will put more pressure on the already dwindling

wetlands of the region


21

8


21

9


Table 6-11: Conversion of Paddy in Kollam 1974 - 2004

Conversion of paddy Area in Hectare Percentage

Paddy to Mixed crops 13381 74.33

Paddy to Rubber 872 4.84

Paddy to wet fallow 2870 15.94

Paddy to built up area 878 4.87

Total 18001 100

The reduction of wetland is a warning as it is referred to as the kidney` of the earth, and also

the place which buffers the effects of floods. The growth of population and lack of proper

land use planning will result in the reduction of wetland area (cf. Map 15, 16).

6.5. Future Land Use – Predictions Based on Markovian Conditional

Probability Statistics

6.5.1 The Land Use

As mentioned in the methodology, the land use maps pertaining to 1973, 1990, 2004 and

2010 of a representative area of 17.65 km2 surrounding the Sasthamkotta Lake was prepared.

A total of 9 land use classes were discernable in the area, they being Class 1- Built up area,

Class 2- Fallow land, Class 3- Mixed vegetation, Class 4- Paddy, Class-5 Rubber, Class 6-

Pond, Class 7- River, Class 8- Sasthamkotta Lake, and Class 9- Laterite quarry. Class 9 was

present only in 2010. These maps were subjected to derive Markovian conditional probability

of transition of being a given class in a projected time in future. The results of this probability

analysis are given below:


6.5.2 Markovian Conditional Probability 1974 -1990 Projected to 2000

The transitional probability of 2000 was estimated by analyzing the available data of 1974

and 1990. Table 6-10 shows the conditional probabilities of individual land use classes for

being a given land use class.

6.5.2.1 Probability of Transition of Land Area under Class 1 in 1974-1990 to Other

Land Use Classes

Table 6-12: Markovian Conditional Probability of Being a Given Land Use Class in the

Year 2000 Predicted Based on Land Use Maps of 1974-1990.

Classes Class 1 Class 2 Class 3 Class 4 Class5 Class 6 Class 7 Class 8

Class 1 0.5618 0.0 0.4047 0.0 0.0 0.0 0.0 0.0334

Class 2 0.169 0.1358 0.5278 0.0302 0.0094 0.0052 0.0 0.1226

Class 3 0.0785 0.0664 0.6986 0.0339 0.0984 0.0201 0.0006 0.0036

Class 4 0.0320 0.0679 0.4086 0.3990 0.0393 0.0473 0.0 0.0058

Class 5 0.0753 0.0295 0.3556 0.1918 0.3578 0.0 0.0 0.0

Class 6 0.1429 0.1429 0.1429 0.1429 0.1429 0.0 0.1429 0.1429

Class 7 0.0 0.0 0.0341 0.0 0.0 0.0 0.9659 0.0

Class 8 0.0391 0.0039 0.0360 0.0294 0.0170 0.0 0.0 0.8747

The overall probability of land area under Class 1 (Built up area) in 1974-1990 (table 6-11),

(Map 17 and 18) to remain as Class 1 was more than 56%while there exists 40% chance for

this land area to change into mixed vegetation (Class 3). This may be explained by the fact

that it is a custom in Kerala to grow at least two or three trees of coconuts, mango, jack fruit

etc in every land holding. This is called homestead farming. Once the canopy of these trees

establish and cover the settlement structures, such land holdings will appear as mixed

vegetation in FCCs/TCCs created from satellite images. The area devoted to build up area

may not be converted to more economically profitable rubber, while vice versa may happen.


-


6.5.2.2. Probability of Transition of Land Area under Class 2 in 1974-1990 to Other

Land Use Classes

There seems to be only 13% chance for Class 2 (Fallow land) land area to remain Class 2.

People may explore possible ways to earn money by planting tapioca, rubber or plantains in

the fallow lands. Because of this there exists 53 % chance for the fallow land to become

mixed vegetation. There is about 16% chance for the fallow land to become built up area

which may be due to the obvious fact that if the land cannot be used for agriculture it may be

used for constructing houses. In short, the area under fallow land may not remain

permanently follow.


Land Use Classes

The probability of Class 3 (Mixed Vegetation) land use area remaining Class 3 is 70%. This

is mainly because of the presence canopy of the trees. Even though the number of households

has increased most of the new buildings are covered by the canopy of the trees of the

homestead and hence this land area is classified as mixed vegetation.


Land Use Classes

The probability of Class 4 (Paddy) remaining as Class 4 is 39%, while there exists 40%

probability for the area becoming mixed vegetation. There exists only 6% chance for paddy

becoming fallow.



Land Use Classes

The probability of Class 5 (Rubber) to remain as Rubber and to change as Class 3 (Mixed

Vegetation) is 35%, each. This shows that area under rubber may remain as rubber in the

projected future too.


Land Use Classes

The probability of Class 6 (Pond) remaining as such is 0% while there is equal probability for

it to change to all other classes. This is due to the depletion of pond area by large scale filling

up of ponds and weed infestation in ponds.


Land Use Classes

The probability of Class 7 (River) to remain river was as high as 97%. A 3% probability

exists for part of it being converted to mixed vegetation which may be nothing but

misclassification errors.


Land Use Classes

The probability of Class 8 (Sasthamkotta Lake) to remain as lake was as much as 87%. As

the shores of the lake vary depending on the water level, some parts may get converted to

other land use classes temporarily. Such conversion will not be long standing and hence

could be ignored.


6.5.2.9. Validation of Projected Probability (1974-1990)

Table 6-13: Validation of Projected Probabilities (2000) Based on the Land Use Map of

2004

Number of Polygons

Class True False

Class 1 47 49

Class 2 3 1

Class 3 92 16

Class 4 4 4

Class 5 45 30

Class 6 3 4

Class 7 1 0

Class 8 1 0

Total 196 104

The table 6.12 ( Annexure. 3) shows the validation of the Markovian probability projection

for the year 2000 based on the 1974 and 1990 land use maps. As it is evident from the table,

the predictions were not realistic for all polygons, especially for those in land use classes 1, 4

and 5. Most reliable prediction was for class 3 which may be attributed to the fact that along

with increase in population and settlement area mixed crop home gardens may have

substantially increased including in land parcels that may be really settlements but classified

in the land use map as mixed vegetation due to high canopy cover and consequent miss

classification which is reflected in the case of large number of falsely predicted polygons in

Class 1. Of the total 300 polygons only 104 polygons are false and 65 % of the polygons are

true to the prediction.


6.5.3. Markovian Conditional Probability 1990-2004 Projected to 2010.

Table 6-14 Markovian Conditional Probability of Being a Given Land Use Class in the

year 2010 Predicted Based on Land Use Maps of 1990-2004

Classes Class 1 Class 2 Class 3 Class 4 Class5 Class 6 Class 7 Class 8

Class 1 0.4147 0.0034 0.4684 0.0097 0.0724 0.0003 0.0 0.0311

Class 2 0.3202 0.0 0.5806 0.0248 0.0688 0.0000 0.0000 0.0056

Class 3 0.1731 0.0077 0.7326 0.0017 0.0669 0.0082 0.0006 0.0092

Class 4 0.3016 0.0426 0.3057 0.3202 0.0070 0.0181 0.0000 0.0048

Class 5 0.3484 0.0170 0.4843 0.0000 0.1332 0.0113 0.0000 0.0058

Class 6 0.1117 0.0225 0.4547 0.0000 0.0012 0.4099 0.0000 0.0000

Class 7 0.0000 0.0000 0.2908 0.0000 0.0123 0.0000 0.6969 0.0000

Class 8 0.0103 0.0000 0.0579 0.0080 0.0103 0.0000 0.0000 0.9136

6.5.3.1 Probability of Transition of Land Area Under Class 1 in 1990-2004 to Other

Land Use Classes

The overall probability of land area under Class 1 (Built up area) in 1990-2004 (table 6-13),

(map 19 and 20) to remain as Class 1 was more than 56% while there exists 40% chance for

this land area to change into mixed vegetation (Class 3).

The probability of being the Class 1 (Built up area) remain as Class 1 was 41 % while there

was 56% in 1974-1990 period but this decrease can be adjusted with the increase in the

percentage of mixed vegetation and rubber together concerned.(table 6.11, figure 6.) The later

part of nineties saw the large scale rubber cultivation around the houses also. Previously

rubber cultivation was done on separate patch of land where there was no houses. The

increasing demand for house construction and the increase in the earnings from rubber

combines this phenomenon.


6.5.3.2. Probability of Transition of Land Area Under Class 2 In 1990-2004 to Other

Land Use Classes

The probability of the Class 2 (Fallow land) being as such was 0%. This was due to large

scale shortage of land for other activities. It was also evident in the probability of being Class

1 (Built up area) it was 16% in previous analysis and it reaches to a level of 32 % almost

doubled during this time the increase in infrastructure development is the main reason behind

this phenomenon. And the probability to converting the area into mixed vegetation is 58 %

while it was 52 % in the previous period. This difference is also attributed to the change in

the land use.


Land Use Classes

The probability of being Class 3 (Mixed vegetation) remains as such is 73% while it was

below 70% in the previous period. The probability being class 1 is 17% while it was only 7 %

in the previous period. This increase is mainly due to the shortage of suitable land for

infrastructure development coupled with economic change.

6.5.3.4. Probability of Transition of Land Area under Class 4 In 1990-2004 To Other

Land Use Classes

The probability of Class 4 (Paddy) area remains as Class 4 is 32% now it was 39% in the

previous period this decrees was because of continuous loss of interest regarding paddy

cultivation. The probability of being used for infrastructure development is more than 30 %

while it was very marginal with 3% in the previous period. The large-scale reclamation is the

main reason behind this. The prevalence of real estate boom forced the dwellers in the dry

area to sell their land and buy some more area in the wetted area and land fill it and construct

a terrace house in the new property. This was evident during the socio economic survey.



Land Use Classes

The probability of Class 5 (Rubber) being as such is 13% while it was 35% in previous

period the demand for more land and rubber mix up with mixed vegetation is main reason

behind this. The probability of being class 4 is 0% while it was 19% in previous period. The

probability of being class 1 is 34 % it was only 7% in the previous period this increase is

mainly attributed to the shortage of available land for constructing houses. The probabilities

of 58% exist for the area being as mixed vegetation it was only 24% previously. This increase

was mainly due to the mix up with other vegetations and in Kerala rubber was grown along

with some trees especially Anjil the trunk of it was used for making durable furniture.


Land Use Classes

The probability of class 6 (Pond) being as such is more than 40 % while it was 0 % in the

previous period. The increase is mainly due to some increase in the vigil of local authority

regarding the reclamation of ponds. The probability of being changed to Class 3 (Mixed

vegetation) is 45 % while it was only 14 % previously. The weed infestation and neglect

towards the ponds were the reasons behind this. There exists 11 % probability that the ponds

are being converted to built up land.

6.5.3.7. Probability of Transition of Land Area Under Class 7 in 1990-2004 to Other

Land Use Classes

Probability of being class 7 ( River) as such is 69% while remaining probability exist for the

area being mixed vegetation almost similar scenario existed in the previous period also.



Land Use Classes

The probability of lake being Class 8 (Sasthamkotta Lake) as such is more than 91% while

remaining probability exist foe the area being changed to mixed vegetation area this was

same in previous period also.

6.5.3.9. Validation of Probability 2010

The probability validation of 2010 shows very varying trend compared to the previous

validation. (See table 6.14, Annexure 4). The class 1 shows that more than 62% chance for

the correctness of the prediction. Only 48% went wrong. Of this 62% more than half of the

polygon shows the prediction of above 50 % accuracy. Thus the Markovian probability of

2010 is very accurate and reliable in future predictions of class 1.

Of the twenty polygons of the class 2 only 6 are true and more than 70% went wrong for the

prediction. Of this 70% all most all the polygons have the lineage towards class 3, i.e. mixed

vegetation. The probability prediction of the Markove showed that all these areas will

eventually become mixed vegetation but that does not come true, and all most all these fallow

lands are previously wetlands. This may be due to the short period taken for the analysis. In

the previous analysis the time interval is 20 years while in the case of this the interval is 14

years.

Of the 214 polygons of this class except 2 polygons all the other polygons are true to the

prediction. This indicates that the future prediction in this regard will also come true. The

overall increase in the mixed vegetation is attributed to the change in the land use that

happened over the years. And this change will dominate in future also.

Class 4 shows equal probability for true and false. Of the 6 polygons at went wrong 5 of them

will become area with mixed vegetation. While in the case of polygons none of the six


polygons have the value more than fifty percentages instead all the polygons have 32%

probability. In short the probability regarding change in paddy field went wrong for the year

2010 concerned. The probabilities of paddy field change to other land uses are very

prominent and in future also it will be difficult to see the same land use regarding the paddy

fields.

Of the 41 polygons of the class 5 category 21 are true and 20 are false. Among the true

polygons more than 50% have 60% probability for being areas with rubber plantation. This

shows that the prediction is not as good as in the case of other land uses. The condition is also

same for the previous validation also. The areas of previous rubber grown area are preserved

as such as it is long duration mono crops that prediction true to the prediction. While

regarding the newer areas the prediction went wrong. The Markove system predicted that that

areas will remain as areas with mixed vegetation and that is not happened here. As mentioned

earlier it is the individual’s decision to cultivate rubber or not in their land. In short the

prediction regarding rubber is not reliable using Markove.

Of the 5 polygons in the case of class 6 all are false. That means the areas under ponds are

predicted to become mixed vegetation. Of the five polygons 3 ponds are newly emerged in

the previous wet fallow lands. Thus the prediction regarding the class 6 is not reliable in

Markove conditional probability. It is highly unlikely to have these ponds as such in future.

The validation of class 8 shows true to present land use that means the lake area will remain

as such in future also. The probability of class 9 shows it is wrong the polygon shows that the

area will remains as mixed vegetation. As the laterite mining is not a natural process it is the

collective decision of individuals to mine an area or not. So it is difficult to project this area

will become laterite as such.


In short out of the 408 polygons only 89 went wrong. In the case of total classes, five classes

are true to the prediction and 4 classes are false. So in the future land use change it can be

assured that these five land use classes will remains as such.

Table 6-15: Validation of Probability 2010

Class True False

Class 1 62 41

Class 2 6 14

Class 3 212 2

Class 4 6 6

Class 5 21 20

Class 6 0 5

Class 7 1 0

Class 8 1 0

Class 9 0 1

Total 319 89

6.5.4. Markovian Conditional Probability 2004- 2010 Projected to 2020


Land Use Classes

The probability of class 1 remains as such is 11% for the last two instances it was 56% and

41% respectively. At the same time the probability to become class three 63 % this was more

than that of the previous periods. This change is not due to the actual reduction in built up

area while the increasing of canopy of domestic trees surrounding the buildings. The change

of 10% is seen in the built up area being changed Kayal it was due to the destruction of some

makeshift buildings along the Kayal.(table 6-15) (map 21 and 22)



Land Use Classes

The probability of being class 2 as such is 0 % it was same in the previous instances also. The

probability of being area with mixed vegetation is 76 % which was more than the previous

instances. The probability of become pond is 10 % all these ponds are due to the digging up

of the wetted area for extracting clay for the bricklin. 13 % of the fallow land being used for

infrastructure development. (Table 6-14)

6.5.4.3. Probability of Transition of Land Area under Class 3 In 2004-2010 to Other

Land Use Classes

The probability of the mixed vegetation being as such is more than 68 % which was very

slight reduction as compared to other periods. The probability of being used for infrastructure

was 10 % only which saw a considerable reduction as compared to other periods. It was due

to increasing price of land forced people to construct houses in the fallow wetland rather the

cultivable and more valuable dry land.


Land Use Classes

The probability of class 4 being remains as such is almost zero. It was same in the previous

instances also. The probability of being fallow is 48 % which was more than that of other

periods. This was due to increasing cost for paddy cultivation coupled with labour shortage.

The probability of paddy fields being used for built up area is 13 % which was lower than the

previous period. There exists a tandem regarding the conversion of paddy field. First it will

become fallow land and then it changes into built up area and at last it will become built up

area. And this process will take more than 7 years.



Land Use Classes

The probability of class 5 as such is only 5%, which was lower than the previous two

instances it was due to increasing fragmentation of land and coming up of more built up area

which plants some mixed trees in the surroundings. Probability to become mixed vegetation

is 68 % which was same in the previous period also same conditions prevails in previous

period influences this period also.

6.5.4.6. Probability of Transition of Land Area Under Class 6 in 2004-2010 to Other

Land Use Classes

The probability of the preserving of the ponds is only a rare possibility only seven percentage

probabilities exist here. The reasons are similar to the previous periods. The probability to

being class 3 is about 65% which was more than that of previous instances. The increasing in

weed population is the main reason behind this.


Land Use Classes

The projection attributes that there is 1oo % probability for the area of river will be under the

canopy of mixed vegetation.


Land Use Classes

The probability to become class 8 is 91% which was greater than the previous periods and 6

% chance exist for the area being enveloped by canopy of trees.

6.5.4.9. Probability of transition of land area under Class 9 in 2004-2010 to other land use

classes


The class nine belongs to laterite quarry. The probability to become laterite quarry in to the

built up area is 100% it was obvious that after mining the area will be converted to construct

houses.

Table 6-16: Markovian Conditional Probability 2004-2010 Transition Probabilities

Classes Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8 Class 9

Class 1 0.1129 0.0390 0.6350 0.0114 0.0536 0.0331 0.0071 0.1058 0.0022

Class 2 0.1300 0.0000 0.7563 0.0059 0.0000 0.1078 0.0000 0.0000 0.0000

Class 3 0.1039 0.0429 0.6846 0.0064 0.0428 0.0171 0.0001 0.1003 0.0019

Class 4 0.1385 0.4857 0.3692 0.0000 0.0067 0.0000 0.0000 0.0000 0.0000

Class 5 0.1055 0.0489 0.6487 0.0000 0.0749 0.0097 0.0179 0.0944 0.0000

Class 6 0.1620 0.0825 0.5998 0.0000 0.1076 0.0481 0.0000 0.0000 0.0000

Class 7 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Class 8 0.0637 0.1622 0.1352 0.0000 0.0397 0.0003 0.0000 0.5986 0.0004

Class 9 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

6.6 Results of Socio Economic Survey

6.6.1 General Details

The survey covered a total of 3880 households of Kunnathur, Sasthamkotta, and West

Kallada panchayaths of Kunnathur Taluk (table 6-15). Of the total respondents 37.7% are

from Sasthamkotta Village, 30.31% are from Kunnathur village and 25.86% from West

Kallada village.

Table 6-17: Participation From Each Panchayaths

Name of Panchayaths

No. of Households Surveyed

Percentage to Total Selected House

holds

Percentage to Total Households of the Village

Kunnathur 1,312 33.8 30.31

Sasthamkotta 1,760 45.4 37.70

West Kallada 808 20.8 25.86

Total 3,880 100.0 100


6.6.2 Size of House holds

Of the total 3880 households surveyed, 22.5% families have less than three members. While

63.6%, families’ with 4 to 5 and 13.9%% have more than 6 family members (table 6-16).

Table 6-18: Size of Households

Number of Persons Frequency Percentage

Up to 3 868 22.5

4 to 5 2468 63.6

6 and above 544 13.9

Total 3880 100.0

6.6.3. Type of Houses

In the case of type of households thatched and sheeted house together contribute 12%. These

are the very low income group of the population. Most of these houses don’t have homestead

farms. Tiled and terraced houses are most common in this area these together accounts for

more than 80% (table 6-18). The dwellers of these houses are middle income and higher

income group and most of them have enough land to do cultivation. The economic condition

of the each household is visible from the type of house they are living.

Table 6-19: Type of Houses

Type of Houses Frequency Percentage

Thatched 352 9.1

Half Thatched and Half Tiled 96 2.5

Tiled 1320 34.0

Half Tiled and Half Terraced 96 2.5

Terraced 1904 49.1

Sheet 112 2.9

Total 3880 100.0

6.6.4 Ownership of Land

In the case of ownership of land about 98% of households have own land and house (Table

6-19). In this regard the occupants with own land accounts for more than 97.9% of the


households. So it is unpredictable how the land use will change over the passage of time.

More and more fragmentation of already fragmented landholdings will be occurring in near

future. This will result in the emergence of new households in the future and it will continue

in coming decades.

Table 6-20: Ownership of Land

Ownership Frequency Percent

Own Land 3800 97.9

Own and Leased Land 32 0.8

Others 48 1.2

Total 3880 100.0

6.6.5. Income

126 households which contributes to 3.24 % of the total households in the study area have a

monthly income of ̀ 20000 and above (table 6-20). Most of these families earning members

are working abroad. These small numbers of households are the drivers of the changes. The

aspiration of other households to reach to the level of these will affect the total land use

changes in the area. They started cultivating rubber in their owned land. The success of this

compelled other to follow them. They are the first to abandon the paddy fields and started

converting it and others follow them.

Table 6-21: Income Range

Income range No. of families Percentage

Less than ̀ 2500 823 21.21

` 2500- ` 5000 1460 37.62

` 5000- ̀ 10000 467 12.03

` 10000- ̀ 15000 702 18.09

` 15000- ̀ 20000 302 7.78

` 20000 and above 126 3.24

Total 3880 100


6.6.6. Occupational Status

The occupation status of the study area reveals that of the total households surveyed, 11.65%,

is cultivators and agriculture labours (table 6.21). Of the total households surveyed home

makers and students accounts for more than 50 %. Excluding home makers, students,

pensioners, etc the participation rate is about 45 %.

Table 6-22: Occupational Status

Occupation Number Percentage

Cultivators and Agricultural

labourers 1020 11.65

Cashew factory labour 414 4.72

Government Servant 312 3.56

Sand and clay mining 232 2.65

Home makers 2352 26.2

Students 2227 25.44

Other services 1864 21.0

Pensioner 432 4.93

Total 8853 100

6.6.7. Place of Occupation

Of the 4664 workers 68.86% are working in their own villages, 10.46% are working in

neighboring villages within the district. About 5% of the workers are working in other

districts. While 6.67% of people are working in other states and 8.83 % in other countries

(table 6-22).

Table 6-23: Place of Occupation

Place of Occupation No. of Persons Percentage

In the village 3212 68.86

Neighboring villages 488 10.46

Out of district 236 5.06

In other states 316 6.77


Abroad 412 8.83

Total 4664 100

6.6.8. Educational Qualification

67.16 % of population of the selected villages has studied up to 9th standard or below. People

who have the education between 10th and 12th standard accounts for 17.64%. Graduates and

post graduates accounts for 10.96% and 2.68% respectively (table 6-23).

Table 6-24: Educational Qualification

Educational Qualification No. of Persons Percentage

Illiterate 166 1.35

Classes 1-9 8512 67.16

10-12 2184 17.64

Degree 1272 10.96

Post graduates 332 2.68

Engineers/ Doctors 62 0.5

Total 12378 100

Comparison of the qualification and education reveals that most of the agriculture labours

have only primary education. While the male members who have passed class 10 have the

tendency to either go for business or go for other service sectors. While in the case of females

their work participation is very meager as compared to that of their male counterparts. In all

areas female outnumber males in the educational qualification of class 10 and above. But this

trend is not reflective in the occupation structure as most of the highly qualified females

eventually become home makers and only a handful enter the service sector or involve in

economically productive activities.

6.6.9. Use of Owned Land

53% of people use their land for cultivation, and built up area accounts for 22 .4%, and 6.4%

of land falls under the category of fallow (table 6-24). Of the 3880 families surveyed more


than 50% of the households use their land for cultivation crops. Nearly 10 % of the

respondents said they do not cultivate any land as of today.

Table 6-25: Use of Owned Land

Land Use Frequency Percent

Cultivated 2064 53.2

Partly Cultivated and Partly Fallow 352 9.1

Homestead farms 304 7.8

Fallow 244 6.3

Fallow and Built up area 48 1.2

Built up area 868 22.4

Total 3880 100.0

6.6.10. Major Crops Cultivated

Coconut is the major crop cultivated in the area which comprises more than 50% of area

under cultivation, followed by rubber which occupies 23% of area. Paddy occupies less than

6 % of the area under cultivation land use. (Table 6-25).

Table 6 -26: Major Crops Cultivated

Crops Area in cents Percentage

Coconut 99520 50.74

Cashew 800 0.39

Paddy 12000 5.99

Tapioca 18864 9.42

Banana 16016 7.99

Rubber 46080 23.01

Others 6944 3.46

Total 200224 100

6.6.11. Main Source of Drinking Water

The people use their own source in the form of wells, tube wells etc. which accounts for

66.68%. Those who are dependent on the public source accounts for about 27% of the


respondents (table 6-26). Those who are directly depended upon the natural source like ponds

and lake accounts for 8.66%. The public water sources include public wells, tube wells, etc.

most of the people who have their own source will also depend on the public water

distribution system of the area. All the public water distribution system of this region depends

on the fresh water from the Sasthamkotta Lake. All these water sources are depended on the

groundwater. The survey reveals that more people are started depending on tube wells as the

severe water shortage in some areas forced them to do so. The large scale depletion of the

wetlands also attributed to the depletion of the water table. If the neglect towards wetland

continues this trend will go on.

Table 6-27: Main Source of Drinking Water

Main Source of Drinking Water Frequency Percentage

Own Source 2592 66.8

Public source 1032 26.59

Natural source 336 8.66

Total 3880 100.0

6.6.12. Leasing of Land

3.8% of people lease their land for agriculture and other uses. While 78.9% does not lease

their land (table 6-27). Of the total number of respondents 148 respondents said they leased

their land for other uses this account for 3.8% of the total respondents (table 6-27, table 6-

28). Of this 148 respondents 51% percentage said they leased the paddy fields due to the

shortage of labour. And 21 % of the respondents revealed that they leased the land for clay

and sand mining. 28% of the respondents said that they leased the paddy fields as they are

economically not profitable. These leased land will eventual depletes further and not suited

for any other crop then it will be land filled and used for constructing houses. Besides this

people also shared that they cannot practice agriculture because of their old age and the new

generation is not willing to take up agriculture as their occupation.


Table 6-28: Leasing of Land

Frequency Percentage Yes 148 3.8 No 3372 96.2 Total 3880 100

Table 6-29: Main Reason for Leasing of Land

Reason Frequency Percent

labour shortage 76 51 %

Economically not profitable 41 28%

leased to a clay mining / sand mining 31 21%

Total 148 100.0

6.6.13. Paddy Cultivation Dynamics

Of the total respondents 71 % said they cultivate paddy twice in a year, while 29% said they

cultivate only single crop in a year (table 6-29). And they keep the land fallow for remaining

time very few cultivate other crops in the interim. The people who are cultivating the paddy

are not sure about the future prospects of the cultivation. And they also revealed that their age

not allowing them to continue with the cultivation. And in no time they will stop cultivating

the paddy unless and until there is any support from the part of government and labours.

Table 6-30: Frequency of Paddy Cultivation in a Year

Frequency Numbers Percentage

Single 128 29 %

Double 320 71 %

Total 448 100

6.6.14. Paddy Cultivation Dynamics

23% of respondents say they converted the paddy fields for other uses (table 6-30). A total of

128 respondent those who cultivate single crop converted their paddy fields for other uses.


The probabilities to convert the paddy fields are highest for those who are practicing double

crops with 70.37 %. In the case of single crop farmers the conversion rate is low with 29.6%

(table 6-30). A total of 13% families cultivate paddy. (See table 6-29, 6-30, 6-31 and table 6-

32.)

Table 6-31: Conversion of Paddy Fields for Other Uses During Last 10 Years

Frequency Percentage

Others 16 0.4

Yes 928 23.9

No 2340 60.3

Total 3284 84.6

6.6.15. Paddy Conversion Dynamics

The total number of respondents who converted the paddy fields for the last 10 years

accounts for 23% of the households. The probabilities to convert the paddy fields are highest

for those who are practicing double crops with 70.37 %. The increasing cost and labour

shortage are the main reason behind this trend. (Table 6-31)

Table 6-32: Paddy Conversion Dynamics

Paddy cultivation

Converted Total Percentage of Total (Converted)

No Yes

Single crop 0 128 128 29.6

Double crop 48 256 304 70.37

Total 48 384 432 100.00

6.6.16. The Purpose to Which the Paddy Fields are Converted

Most number of the conversions took place for the cultivation of tapioca, followed by rubber

these together contribute to more than 40 % of total converted lands. About 10% area was

used for building houses (table 6-32).


Table 6-33: Purpose to Which the Paddy Fields are Converted

Purpose Number of respondents Area (in cents) Percentage of Area

Rubber planting 48 820 19.85

Sand mining 18 400 9.68

Clay mining 17 200 4.84

Building houses 56 400 9.68

Tapioca

cultivation 107 900 21.8

Banana


Coconut


Vegetable

cultivation 32 450 11

Total 432 4130 100

22% of the respondents reveled that they convert the paddy field for cultivating tapioca,

19.85 % of those convert the paddy fields for planting rubber. A total of 12% of respondents

reveals that they converted their paddy fields for mining clay and sand. This large scale sand

and clay mining will affect the hydrological characteristics of the wetland and eventually it

will start to affect the environment. About 10 % of the people constructed their hose in the

land filled area of former paddy fields. More than 14 % of the respondents revealed that they

converted the paddy fields for coconut cultivation. If this trend will continue for long it will

affect the healthy existence of the wetlands.

6.6.17. Changes in Area under Wetlands

The respondents were asked their perception regarding the change in the area under wetlands.

Regarding the wetlands loss 94 % of the respondents are ware aware about it. (Table 6-33).


Table 6-34: Changes in Area under Wetlands

Is the Area Under Wetland

Decreasing? No of respondents Percentage

Yes 3656 94.22

Do not know 224 5.77

Total 3880 100

6.6.18. The Reason behind Decrease of Wetland Area

Interestingly 2.46 % of the respondents who reside very close to the wetlands say that it is

after the bunding the paddy cultivation started decreasing (table-6-34). They are older

generation with over 70 years of age. They insist that until the construction of the bund it was

very easier to maintain the water level in the paddy field. But after the bunding the severe

water shortage and occurrences of floods discouraged the farmers from doing paddy

cultivation, and they slowly started abandoning the cultivation.

25% of the respondents say it was the severe economic shortage forced the cultivators to

move away from the paddy cultivation due to the lack of support from government and local

bodies. The ever increasing price of seeds and fertilizers and increasing labour cost are

attributed to the loss of paddy field. The ever increasing labour cost in the paddy cultivation

forced the cultivators to cultivate paddy once in a year or ultimately to change to other crops

or keep it as fallow.

20% of the respondents there is no such economic problem or labour shortage exist instead

lack of enthusiasm of young generation towards the cultivation. Even though they will not get

any job with their educational qualification they are very hesitant to do the paddy cultivation.

They feel that these are for older generation.


19% of the respondents said it is the rubber that changed the paddy fields entirely some even

predicted that within no time the entire paddy fields of Sasthamkotta region will come under

rubber cultivation.

Table 6-35 Reason behind Decrease of Wetland

Reason Behind Reduction of Paddy Fields Total Respondents Percentage

Bunding of Kayal 90 2.46

Economic reason 915 25

Lack of enthusiasm from young generation 756 20.52

Converting due to rubber 700 19.14

Briklins, clay and sand mining 292 8

Labour shortage 933 25.51

Total Respondents 3656 100

6.6.19. Use of the Kayal And its Surrounding

Of the 61% of the hose holds who use Sasthamkotta Lake directly 46% use it for drinking

purpose. While 2.1% use the Kayal and its surroundings for sand mining. 39% of the

respondents say they do not use the Kayal and its environment (table 6-35). The large scale

dependency of Kayal for water will continues in future also.

Table 6-36: Use of the Kayal and its Surrounding for Your Day today Activities

Uses Frequency Percentage

Drinking Water 1800 46.4

Fishing 96 2.5

Clay / Sand Mining 368 9.5

Others 80 2.1

Drinking Water and sand mining 16 0.4

Total 2376 61.2

Do not Use 1520 39.2

Grand Total 3880 100.0


6.6.20. Quality of Kayal water

The respondents were asked to assess the quality of kayal water. Of the 898 households the

whopping 46% says the quality of Kayal water is very average about 4% of them each are

two extremes (table 6-36). Only 3% of the people are highly confident about the quality of

the water (table 6.35). 4% of the respondents say they feel the quality water is very bad.

While almost all of them they are somewhat comfortable with the water quality.

Table 6-37: The Quality of Kayal Water

Quality of Kayal Water Frequency Percentage

Very Good 144 3.71

Good 1512 39.0

Average 1776 45.8

Bad 160 4.1

Total 3592 92.6

No response 288 7.4

Grant Total 3880 100.0

Thus the wetlands of Kollam face a multitude of problems. The geo-climatic factors are not

as important in the district as the district is getting an average rainfall over the last 35 years.

But the population change combined with the change in land use drastically affect the natural

environment of Kollam especially the wetlands. The area under paddy fields and wetlands

shows a very rapid decline over the last few decades. The change in occupation pattern,

lifestyle change, change in people’s perception on environment and government policies are

the main result behind this decline. If this trend is going on the quality as well as the quantity

of wetland will decline. We are in a point of no return regarding the status of wetland in

Kollam district.

chapter v methodology - shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/12677/12/12_chapter...

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